Hands-on ML Ops Support Engineer

CodeRound; Hinoba-an; On-site; PHP 400,000 - 600,000; Full time

Job summary

CodeRound is seeking a Technical Support Engineer with a strong focus on MLOps to provide enterprise-level assistance to our customers. You will diagnose and troubleshoot issues related to machine learning models, frameworks, and deployment pipelines to keep ML systems running smoothly.

You will deliver remote support, communicate clearly via email, chat, and phone, and collaborate with data science teams to resolve issues and share MLOps best practices.

Qualifications

• Experience diagnosing ML model issues and deployment pipelines.
• Knowledge of ML frameworks and related tools.
• Ability to communicate technical information clearly to clients.

Responsibilities

• Own customer issues related to ML applications and drive resolution.
• Research, diagnose, and troubleshoot ML software and hardware issues.
• Provide support for ML frameworks (TensorFlow, PyTorch) and tools (MLflow, Kubeflow).
• Assist with network configuration affecting ML performance and data access.
• Use remote desktop to provide immediate client support.
• Communicate via email, chat, and phone with clear guidance.
• Collaborate with Customer Success Manager and data science teams.
• Escalate unresolved issues to internal teams when needed.
• Document knowledge as notes and manuals on MLOps.
• Follow up with clients after troubleshooting.

Skills

Troubleshooting

Remote support

Communication

Customer focus

Tools

TensorFlow

PyTorch

MLflow

Kubeflow

SSH/Remote Desktop

Linux basics

Job description

CodeRound is seeking a Technical Support Engineer with a strong focus on MLOps to provide enterprise-level assistance to our customers. You will diagnose and troubleshoot issues related to machine learning models, frameworks, and deployment pipelines to keep ML systems running smoothly.

You will deliver remote support, communicate clearly via email, chat, and phone, and collaborate with data science teams to resolve issues and share MLOps best practices.